Systems and method for determination and display of personalized distance

ABSTRACT

A system and method for determination and display of personalized distance. A request is received for the determination of a personalized distance over a network, wherein the request comprises an identification of a requesting user, and a plurality of real world entities comprising at least a starting location and an ending location. At least one route is determined between the first location and the second location. Spatial, temporal, topical, and social data available to the network relating to the requesting user and each real world entity and the route is retrieved using a global index of data available to the network. A personalized distance is calculated via the network between the first location and the second location using spatial, temporal, topical, and social data relating to the requesting user and each real world entity and the route. A representation of the personalized distance calculated for the route is displayed on a display medium.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a continuation of, and claims priority from U.S.patent application Ser. No. 14/144,981, filed on Dec. 31, 2013, entitled“Systems and Method for Determination and Display of PersonalizedDistance,” which is a continuation of and claims priority from U.S.patent application Ser. No. 12/163,249, filed on Jun. 27, 2008, now U.S.Pat. No. 8,706,406, entitled “Systems and Method for Determination andDisplay of Personalized Distance”, the entirety of which areincorporated herein by reference.

This application includes material which is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent disclosure, as it appears in thePatent and Trademark Office files or records, but otherwise reserves allcopyright rights whatsoever.

FIELD OF THE INVENTION

The present invention relates to systems and methods for selecting andpresenting media on a network and, more particularly, to systems andmethods for selecting and presenting media on a network to tune anenvironment to the tastes of the users within that environment, thusimproving the experience of that location.

BACKGROUND OF THE INVENTION

A great deal of information is generated when people use electronicdevices, such as when people use mobile phones and cable set-top boxes.Such information, such as location, applications used, social network,physical and online locations visited, to name a few, could be used todeliver useful services and information to end users, and providecommercial opportunities to advertisers and retailers. However, most ofthis information is effectively abandoned due to deficiencies in the waysuch information can be captured. For example, and with respect to amobile phone, information is generally not gathered while the mobilephone is idle (i.e., not being used by a user). Other information, suchas presence of others in the immediate vicinity, time and frequency ofmessages to other users, and activities of a user's social network arealso not captured effectively.

SUMMARY OF THE INVENTION

In one embodiment, the invention is directed to a method. A request isreceived for the determination of a personalized distance over anetwork, wherein the request comprises an identification of a requestinguser, and a plurality of real world entities comprising at least astarting location and an ending location. At least one route isdetermined between the first location and the second location. Spatial,temporal, topical, and social data available to the network relating tothe requesting user and each real world entity and the route isretrieved using a global index of data available to the network. Apersonalized distance is calculated via the network between the firstlocation and the second location using spatial, temporal, topical, andsocial data relating to the requesting user and each real world entityand the route. A representation of the personalized distance calculatedfor the route is displayed on a display medium.

In another embodiment, the invention is directed to a system. The systemcomprises: a request receiving module that receives requests for thecalculation of personalized distances between real-world entities,wherein the request comprises a requesting user and a plurality ofreal-world entities comprising at least a starting location and anending location; a route determination module that maps at least oneroute between starting locations and ending locations for each requestreceived by the request receiving module; a route data retrieval modulethat retrieves spatial, temporal, topical, and social data available tothe network relating to the requesting user and each real world entityand the route using a global index of data available to the network; apersonalized distance calculation module that uses the data retrieved bythe route data retrieval module to calculate a personalized distance forroutes mapped by the route determination module; and a personalizeddistance display module that displays personalized distance calculatedby the personalized distance calculation module on a display medium.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of theinvention will be apparent from the following more particulardescription of preferred embodiments as illustrated in the accompanyingdrawings, in which reference characters refer to the same partsthroughout the various views. The drawings are not necessarily to scale,emphasis instead being placed upon illustrating principles of theinvention.

FIG. 1 illustrates relationships between real-world entities (RWE) andinformation objects (IO) on one embodiment of a W4 CommunicationsNetwork (W4 COMN.)

FIG. 2 illustrates metadata defining the relationships between RWEs andIOs on one embodiment of a W4 COMN.

FIG. 3 illustrates a conceptual model of one embodiment of a W4 COMN.

FIG. 4 illustrates the functional layers of one embodiment of the W4COMN architecture.

FIG. 5 illustrates the analysis components of one embodiment of a W4engine as shown in FIG. 2.

FIG. 6 illustrates one embodiment of a W4 engine showing differentcomponents within the sub-engines shown in FIG. 5.

FIG. 7. illustrates one embodiment of the use of a W4 COMN for thedetermination of personalized distances between two or more real worldobjects

FIG. 8 illustrates one embodiment of how the users and devices shown inFIG. 7 can be defined to a W4 COMN.

FIG. 9 illustrates one embodiment of a data model showing how the RWEsshown in FIG. 8 can be related to entities and objects within a W4 COMN.

FIG. 10 illustrates one embodiment of a process 900 of how a networkhaving temporal, spatial, and social data, for example, a W4 COMN, canbe used for the determination of personalized distances between two ormore real world objects.

FIG. 11 illustrates one embodiment of a personal distance determinationengine 1000 that is capable of supporting the process in FIG. 10.

FIG. 12 illustrates a user interface for adjusting the weights ofspatial, temporal, social and topical factors in a personalized distancecalculation.

DETAILED DESCRIPTION

The present invention is described below with reference to blockdiagrams and operational illustrations of methods and devices to selectand present media related to a specific topic. It is understood thateach block of the block diagrams or operational illustrations, andcombinations of blocks in the block diagrams or operationalillustrations, can be implemented by means of analog or digital hardwareand computer program instructions.

These computer program instructions can be provided to a processor of ageneral purpose computer, special purpose computer, ASIC, or otherprogrammable data processing apparatus, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, implements the functions/acts specified inthe block diagrams or operational block or blocks.

In some alternate implementations, the functions/acts noted in theblocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and applications software which supportthe services provided by the server.

For the purposes of this disclosure the term “end user” or “user” shouldbe understood to refer to a consumer of data supplied by a dataprovider. By way of example, and not limitation, the term “end user” canrefer to a person who receives data provided by the data provider overthe Internet in a browser session, or can refer to an automated softwareapplication which receives the data and stores or processes the data.

For the purposes of this disclosure the term “media” and “media content”should be understood to refer to binary data which contains contentwhich can be of interest to an end user. By way of example, and notlimitation, the term “media” and “media content” can refer to multimediadata, such as video data or audio data, or any other form of datacapable of being transformed into a form perceivable by an end user.Such data can, furthermore, be encoded in any manner currently known, orwhich can be developed in the future, for specific purposes. By way ofexample, and not limitation, the data can be encrypted, compressed,and/or can contained embedded metadata.

For the purposes of this disclosure, a computer readable medium storescomputer data in machine readable form. By way of example, and notlimitation, a computer readable medium can comprise computer storagemedia and communication media. Computer storage media includes volatileand non-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EPROM, EEPROM, flash memory or other solid-state memory technology,CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetictape, magnetic disk storage or other mass storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by the computer.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module may be stored on a computer readable medium. Modules may beintegral to one or more servers, or be loaded and executed by one ormore servers. One or more modules may grouped into an engine or anapplication.

For the purposes of this disclosure an engine is a software, hardware,or firmware (or combinations thereof) system, process or functionalitythat performs or facilitates the processes, features, and/or functionsdescribed herein (with or without human interaction or augmentation).

Embodiments of the present invention utilize information provided by anetwork which is capable of providing data collected and stored bymultiple devices on a network. Such information may include, withoutlimitation, temporal information, spatial information, and userinformation relating to a specific user or hardware device. Userinformation may include, without limitation, user demographics, userpreferences, user social networks, and user behavior. One embodiment ofsuch a network is a W4 Communications Network.

A “W4 Communications Network” or W4 COMN, provides information relatedto the “Who, What, When and Where” of interactions within the network.In one embodiment, the W4 COMN is a collection of users, devices andprocesses that foster both synchronous and asynchronous communicationsbetween users and their proxies providing an instrumented network ofsensors providing data recognition and collection in real-worldenvironments about any subject, location, user or combination thereof.

In one embodiment, the W4 COMN can handle the routing/addressing,scheduling, filtering, prioritization, replying, forwarding, storing,deleting, privacy, transacting, triggering of a new message, propagatingchanges, transcoding and linking. Furthermore, these actions can beperformed on any communication channel accessible by the W4 COMN.

In one embodiment, the W4 COMN uses a data modeling strategy forcreating profiles for not only users and locations, but also any deviceon the network and any kind of user-defined data with user-specifiedconditions. Using Social, Spatial, Temporal and Logical data availableabout a specific user, topic or logical data object, every entity knownto the W4 COMN can be mapped and represented against all other knownentities and data objects in order to create both a micro graph forevery entity as well as a global graph that relates all known entitieswith one another. In one embodiment, such relationships between entitiesand data objects are stored in a global index within the W4 COMN.

In one embodiment, a W4 COMN network relates to what may be termed“real-world entities”, hereinafter referred to as RWEs. A RWE refers to,without limitation, a person, device, location, or other physical thingknown to a W4 COMN. In one embodiment, each RWE known to a W4 COMN isassigned a unique W4 identification number that identifies the RWEwithin the W4 COMN.

RWEs can interact with the network directly or through proxies, whichcan themselves be RWEs. Examples of RWEs that interact directly with theW4 COMN include any device such as a sensor, motor, or other piece ofhardware connected to the W4 COMN in order to receive or transmit dataor control signals. RWE may include all devices that can serve asnetwork nodes or generate, request and/or consume data in a networkedenvironment or that can be controlled through a network. Such devicesinclude any kind of “dumb” device purpose-designed to interact with anetwork (e.g., cell phones, cable television set top boxes, faxmachines, telephones, and radio frequency identification (RFID) tags,sensors, etc.).

Examples of RWEs that may use proxies to interact with W4 COMN networkinclude non-electronic entities including physical entities, such aspeople, locations (e.g., states, cities, houses, buildings, airports,roads, etc.) and things (e.g., animals, pets, livestock, gardens,physical objects, cars, airplanes, works of art, etc.), and intangibleentities such as business entities, legal entities, groups of people orsports teams. In addition, “smart” devices (e.g., computing devices suchas smart phones, smart set top boxes, smart cars that supportcommunication with other devices or networks, laptop computers, personalcomputers, server computers, satellites, etc.) may be considered RWEthat use proxies to interact with the network, where softwareapplications executing on the device that serve as the devices' proxies.

In one embodiment, a W4 COMN may allow associations between RWEs to bedetermined and tracked. For example, a given user (an RWE) can beassociated with any number and type of other RWEs including otherpeople, cell phones, smart credit cards, personal data assistants, emailand other communication service accounts, networked computers, smartappliances, set top boxes and receivers for cable television and othermedia services, and any other networked device. This association can bemade explicitly by the user, such as when the RWE is installed into theW4 COMN.

An example of this is the set up of a new cell phone, cable televisionservice or email account in which a user explicitly identifies an RWE(e.g., the user's phone for the cell phone service, the user's set topbox and/or a location for cable service, or a username and password forthe online service) as being directly associated with the user. Thisexplicit association can include the user identifying a specificrelationship between the user and the RWE (e.g., this is my device, thisis my home appliance, this person is my friend/father/son/etc., thisdevice is shared between me and other users, etc.). RWEs can also beimplicitly associated with a user based on a current situation. Forexample, a weather sensor on the W4 COMN can be implicitly associatedwith a user based on information indicating that the user lives or ispassing near the sensor's location.

In one embodiment, a W4 COMN network may additionally include what maybe termed “information-objects”, hereinafter referred to as IOs. Aninformation object (IO) is a logical object that may store, maintain,generate or otherwise provides data for use by RWEs and/or the W4 COMN.In one embodiment, data within in an IO can be revised by the act of anRWE An IO within in a W4 COMN can be provided a unique W4 identificationnumber that identifies the IO within the W4 COMN.

In one embodiment, IOs include passive objects such as communicationsignals (e.g., digital and analog telephone signals, streaming media andinterprocess communications), email messages, transaction records,virtual cards, event records (e.g., a data file identifying a time,possibly in combination with one or more RWEs such as users andlocations, that can further be associated with a knowntopic/activity/significance such as a concert, rally, meeting, sportingevent, etc.), recordings of phone calls, calendar entries, web pages,database entries, electronic media objects (e.g., media files containingsongs, videos, pictures, images, audio messages, phone calls, etc.),electronic files and associated metadata.

In one embodiment, IOs include any executing process or application thatconsumes or generates data such as an email communication application(such as OUTLOOK by MICROSOFT, or YAHOO!MAIL by YAHOO!), a calendaringapplication, a word processing application, an image editingapplication, a media player application, a weather monitoringapplication, a browser application and a web page server application.Such active IOs can or can not serve as a proxy for one or more RWEs.For example, voice communication software on a smart phone can serve asthe proxy for both the smart phone and for the owner of the smart phone.

In one embodiment, for every IO there are at least three classes ofassociated RWEs. The first is the RWE that owns or controls the IO,whether as the creator or a rights holder (e.g., an RWE with editingrights or use rights to the IO). The second is the RWE(s) that the IOrelates to, for example by containing information about the RWE or thatidentifies the RWE. The third are any RWEs that access the IO in orderto obtain data from the IO for some purpose.

Within the context of a W4 COMN, “available data” and “W4 data” meansdata that exists in an IO or data that can be collected from a known IOor RWE such as a deployed sensor. Within the context of a W4 COMN,“sensor” means any source of W4 data including PCs, phones, portable PCsor other wireless devices, household devices, cars, appliances, securityscanners, video surveillance, RFID tags in clothes, products andlocations, online data or any other source of information about areal-world user/topic/thing (RWE) or logic-basedagent/process/topic/thing (IO).

FIG. 1 illustrates one embodiment of relationships between RWEs and IOson a W4 COMN. A user 102 is a RWE provided with a unique network ID. Theuser 102 may be a human that communicates with the network using proxydevices 104, 106, 108, 110 associated with the user 102, all of whichare RWEs having a unique network ID. These proxies can communicatedirectly with the W4 COMN or can communicate with the W4 COMN using IOssuch as applications executed on or by a proxy device.

In one embodiment, the proxy devices 104, 106, 108, 110 can beexplicitly associated with the user 102. For example, one device 104 canbe a smart phone connected by a cellular service provider to the networkand another device 106 can be a smart vehicle that is connected to thenetwork. Other devices can be implicitly associated with the user 102.

For example, one device 108 can be a “dumb” weather sensor at a locationmatching the current location of the user's cell phone 104, and thusimplicitly associated with the user 102 while the two RWEs 104, 108 areco-located. Another implicitly associated device 110 can be a sensor 110for physical location 112 known to the W4 COMN. The location 112 isknown, either explicitly (through a user-designated relationship, e.g.,this is my home, place of employment, parent, etc.) or implicitly (theuser 102 is often co-located with the RWE 112 as evidenced by data fromthe sensor 110 at that location 112), to be associated with the firstuser 102.

The user 102 can be directly associated with one or more persons 140,and indirectly associated with still more persons 142, 144 through achain of direct associations. Such associations can be explicit (e.g.,the user 102 can have identified the associated person 140 as his/herfather, or can have identified the person 140 as a member of the user'ssocial network) or implicit (e.g., they share the same address).Tracking the associations between people (and other RWEs as well) allowsthe creation of the concept of “intimacy”, where intimacy may be definedas a measure of the degree of association between two people or RWEs.For example, each degree of removal between RWEs can be considered alower level of intimacy, and assigned lower intimacy score. Intimacy canbe based solely on explicit social data or can be expanded to includeall W4 data including spatial data and temporal data.

In one embodiment, each RWE 102, 104, 106, 108, 110, 112, 140, 142, 144of a W4 COMN can be associated with one or more IOs as shown. FIG. 1illustrates two IOs 122, 124 as associated with the cell phone device104. One IO 122 can be a passive data object such as an event recordthat is used by scheduling/calendaring software on the cell phone, acontact IO used by an address book application, a historical record of atransaction made using the device 104 or a copy of a message sent fromthe device 104. The other IO 124 can be an active software process orapplication that serves as the device's proxy to the W4 COMN bytransmitting or receiving data via the W4 COMN. Voice communicationsoftware, scheduling/calendaring software, an address book applicationor a text messaging application are all examples of IOs that cancommunicate with other IOs and RWEs on the network. IOs may additionallyrelate to topics of interest to one or more RWEs, such topics including,without limitation, musical artists, genera of music, a location and soforth.

The IOs 122, 124 can be locally stored on the device 104 or storedremotely on some node or datastore accessible to the W4 COMN, such as amessage server or cell phone service datacenter. The IO 126 associatedwith the vehicle 108 can be an electronic file containing thespecifications and/or current status of the vehicle 108, such as make,model, identification number, current location, current speed, currentcondition, current owner, etc. The IO 128 associated with sensor 108 canidentify the current state of the subject(s) monitored by the sensor108, such as current weather or current traffic. The IO 130 associatedwith the cell phone 110 can be information in a database identifyingrecent calls or the amount of charges on the current bill.

RWEs which can only interact with the W4 COMN through proxies, such aspeople 102, 140, 142, 144, computing devices 104, 106 and locations 112,can have one or more IOs 132, 134, 146, 148, 150 directly associatedwith them which contain RWE-specific information for the associated RWE.For example, IOs associated with a person 132, 146, 148, 150 can includea user profile containing email addresses, telephone numbers, physicaladdresses, user preferences, identification of devices and other RWEsassociated with the user. The IOs may additionally include records ofthe user's past interactions with other RWE's on the W4 COMN (e.g.,transaction records, copies of messages, listings of time and locationcombinations recording the user's whereabouts in the past), the uniqueW4 COMN identifier for the location and/or any relationship information(e.g., explicit user-designations of the user's relationships withrelatives, employers, co-workers, neighbors, service providers, etc.).

Another example of IOs associated with a person 132, 146, 148, 150includes remote applications through which a person can communicate withthe W4 COMN such as an account with a web-based email service such asYahoo! Mail. A location's IO 134 can contain information such as theexact coordinates of the location, driving directions to the location, aclassification of the location (residence, place of business, public,non-public, etc.), information about the services or products that canbe obtained at the location, the unique W4 COMN identifier for thelocation, businesses located at the location, photographs of thelocation, etc.

In one embodiment, RWEs and IOs are correlated to identify relationshipsbetween them. RWEs and IOs may be correlated using metadata. Forexample, if an IO is a music file, metadata for the file can includedata identifying the artist, song, etc., album art, and the format ofthe music data. This metadata can be stored as part of the music file orin one or more different IOs that are associated with the music file orboth. W4 metadata can additionally include the owner of the music fileand the rights the owner has in the music file. As another example, ifthe IO is a picture taken by an electronic camera, the picture caninclude in addition to the primary image data from which an image can becreated on a display, metadata identifying when the picture was taken,where the camera was when the picture was taken, what camera took thepicture, who, if anyone, is associated (e.g., designated as the camera'sowner) with the camera, and who and what are the subjects of/in thepicture. The W4 COMN uses all the available metadata in order toidentify implicit and explicit associations between entities and dataobjects.

FIG. 2 illustrates one embodiment of metadata defining the relationshipsbetween RWEs and IOs on the W4 COMN. In the embodiment shown, an IO 202includes object data 204 and five discrete items of metadata 206, 208,210, 212, 214. Some items of metadata 208, 210, 212 can containinformation related only to the object data 204 and unrelated to anyother IO or RWE. For example, a creation date, text or an image that isto be associated with the object data 204 of the IO 202.

Some of items of metadata 206, 214, on the other hand, can identifyrelationships between the IO 202 and other RWEs and IOs. As illustrated,the IO 202 is associated by one item of metadata 206 with an RWE 220that RWE 220 is further associated with two IOs 224, 226 and a secondRWE 222 based on some information known to the W4 COMN. For example,could describe the relations between an image (IO 202) containingmetadata 206 that identifies the electronic camera (the first RWE 220)and the user (the second RWE 224) that is known by the system to be theowner of the camera 220. Such ownership information can be determined,for example, from one or another of the IOs 224, 226 associated with thecamera 220.

FIG. 2 also illustrates metadata 214 that associates the IO 202 withanother IO 230. This IO 230 is itself associated with three other IOs232, 234, 236 that are further associated with different RWEs 242, 244,246. This part of FIG. 2, for example, could describe the relationsbetween a music file (IO 202) containing metadata 206 that identifiesthe digital rights file (the first IO 230) that defines the scope of therights of use associated with this music file 202. The other IOs 232,234, 236 are other music files that are associated with the rights ofuse and which are currently associated with specific owners (RWEs 242,244, 246).

FIG. 3 illustrates one embodiment a conceptual model of a W4 COMN. TheW4 COMN 300 creates an instrumented messaging infrastructure in the formof a global logical network cloud conceptually sub-divided intonetworked-clouds for each of the 4Ws: Who, Where, What and When. In theWho cloud 302 are all users whether acting as senders, receivers, datapoints or confirmation/certification sources as well as user proxies inthe forms of user-program processes, devices, agents, calendars, etc.

In the Where cloud 304 are all physical locations, events, sensors orother RWEs associated with a spatial reference point or location. TheWhen cloud 306 is composed of natural temporal events (that is eventsthat are not associated with particular location or person such as days,times, seasons) as well as collective user temporal events (holidays,anniversaries, elections, etc.) and user-defined temporal events(birthdays, smart-timing programs).

The What cloud 308 is comprised of all known data—web or private,commercial or user—accessible to the W4 COMN, including for exampleenvironmental data like weather and news, RWE-generated data, IOs and IOdata, user data, models, processes and applications. Thus, conceptually,most data is contained in the What cloud 308.

Some entities, sensors or data may potentially exist in multiple cloudseither disparate in time or simultaneously. Additionally, some IOs andRWEs can be composites in that they combine elements from one or moreclouds. Such composites can be classified as appropriate to facilitatethe determination of associations between RWEs and IOs. For example, anevent consisting of a location and time could be equally classifiedwithin the When cloud 306, the What cloud 308 and/or the Where cloud304.

In one embodiment, a W4 engine 310 is center of the W4 COMN'sintelligence for making all decisions in the W4 COMN. The W4 engine 310controls all interactions between each layer of the W4 COMN and isresponsible for executing any approved user or application objectiveenabled by W4 COMN operations or interoperating applications. In anembodiment, the W4 COMN is an open platform with standardized, publishedAPIs for requesting (among other things) synchronization,disambiguation, user or topic addressing, access rights, prioritizationor other value-based ranking, smart scheduling, automation and topical,social, spatial or temporal alerts.

One function of the W4 COMN is to collect data concerning allcommunications and interactions conducted via the W4 COMN, which caninclude storing copies of IOs and information identifying all RWEs andother information related to the IOs (e.g., who, what, when, whereinformation). Other data collected by the W4 COMN can includeinformation about the status of any given RWE and IO at any given time,such as the location, operational state, monitored conditions (e.g., foran RWE that is a weather sensor, the current weather conditions beingmonitored or for an RWE that is a cell phone, its current location basedon the cellular towers it is in contact with) and current status.

The W4 engine 310 is also responsible for identifying RWEs andrelationships between RWEs and IOs from the data and communicationstreams passing through the W4 COMN. The function of identifying RWEsassociated with or implicated by IOs and actions performed by other RWEsmay be referred to as entity extraction. Entity extraction can includeboth simple actions, such as identifying the sender and receivers of aparticular IO, and more complicated analyses of the data collected byand/or available to the W4 COMN, for example determining that a messagelisted the time and location of an upcoming event and associating thatevent with the sender and receiver(s) of the message based on thecontext of the message or determining that an RWE is stuck in a trafficjam based on a correlation of the RWE's location with the status of aco-located traffic monitor.

It should be noted that when performing entity extraction from an IO,the IO can be an opaque object with only where only W4 metadata relatedto the object is visible, but internal data of the IO (i.e., the actualprimary or object data contained within the object) are not, and thusmetadata extraction is limited to the metadata. Alternatively, ifinternal data of the IO is visible, it can also be used in entityextraction, e.g. strings within an email are extracted and associated asRWEs to for use in determining the relationships between the sender,user, topic or other RWE or IO impacted by the object or process.

In the embodiment shown, the W4 engine 310 can be one or a group ofdistributed computing devices, such as a general-purpose personalcomputers (PCs) or purpose built server computers, connected to the W4COMN by communication hardware and/or software. Such computing devicescan be a single device or a group of devices acting together. Computingdevices can be provided with any number of program modules and datafiles stored in a local or remote mass storage device and local memory(e.g., RAM) of the computing device. For example, as mentioned above, acomputing device can include an operating system suitable forcontrolling the operation of a networked computer, such as the WINDOWSXP or WINDOWS SERVER operating systems from MICROSOFT CORPORATION.

Some RWEs can also be computing devices such as, without limitation,smart phones, web-enabled appliances, PCs, laptop computers, andpersonal data assistants (PDAs). Computing devices can be connected toone or more communications networks such as the Internet, a publiclyswitched telephone network, a cellular telephone network, a satellitecommunication network, a wired communication network such as a cabletelevision or private area network. Computing devices can be connectedany such network via a wired data connection or wireless connection suchas a wi-fi, a WiMAX (802.36), a Bluetooth or a cellular telephoneconnection.

Local data structures, including discrete IOs, can be stored on acomputer-readable medium (not shown) that is connected to, or part of,any of the computing devices described herein including the W4 engine310. For example, in one embodiment, the data backbone of the W4 COMN,discussed below, includes multiple mass storage devices that maintainthe IOs, metadata and data necessary to determine relationships betweenRWEs and IOs as described herein.

FIG. 4 illustrates one embodiment of the functional layers of a W4 COMNarchitecture. At the lowest layer, referred to as the sensor layer 402,is the network 404 of the actual devices, users, nodes and other RWEs.Sensors include known technologies like web analytics, GPS, cell-towerpings, use logs, credit card transactions, online purchases, explicituser profiles and implicit user profiling achieved through behavioraltargeting, search analysis and other analytics models used to optimizespecific network applications or functions.

The data layer 406 stores and catalogs the data produced by the sensorlayer 402. The data can be managed by either the network 404 of sensorsor the network infrastructure 406 that is built on top of theinstrumented network of users, devices, agents, locations, processes andsensors. The network infrastructure 408 is the core under-the-coversnetwork infrastructure that includes the hardware and software necessaryto receive that transmit data from the sensors, devices, etc. of thenetwork 404. It further includes the processing and storage capabilitynecessary to meaningfully categorize and track the data created by thenetwork 404.

The user profiling layer 410 performs the W4 COMN's user profilingfunctions. This layer 410 can further be distributed between the networkinfrastructure 408 and user applications/processes 412 executing on theW4 engine or disparate user computing devices. Personalization isenabled across any single or combination of communication channels andmodes including email, IM, texting (SMS, etc.), photobloging, audio(e.g. telephone call), video (teleconferencing, live broadcast), games,data confidence processes, security, certification or any other W4 COMMprocess call for available data.

In one embodiment, the user profiling layer 410 is a logic-based layerabove all sensors to which sensor data are sent in the rawest form to bemapped and placed into the W4 COMN data backbone 420. The data(collected and refined, related and deduplicated, synchronized anddisambiguated) are then stored in one or a collection of relateddatabases available applications approved on the W4 COMN.Network-originating actions and communications are based upon the fieldsof the data backbone, and some of these actions are such that theythemselves become records somewhere in the backbone, e.g. invoicing,while others, e.g. fraud detection, synchronization, disambiguation, canbe done without an impact to profiles and models within the backbone.

Actions originating from outside the network, e.g., RWEs such as users,locations, proxies and processes, come from the applications layer 414of the W4 COMN. Some applications can be developed by the W4 COMNoperator and appear to be implemented as part of the communicationsinfrastructure 408, e.g. email or calendar programs because of howclosely they operate with the sensor processing and user profiling layer410. The applications 412 also serve as a sensor in that they, throughtheir actions, generate data back to the data layer 406 via the databackbone concerning any data created or available due to theapplications execution.

In one embodiment, the applications layer 414 can also provide a userinterface (UI) based on device, network, carrier as well asuser-selected or security-based customizations. Any UI can operatewithin the W4 COMN if it is instrumented to provide data on userinteractions or actions back to the network. In the case of W4 COMNenabled mobile devices, the UI can also be used to confirm ordisambiguate incomplete W4 data in real-time, as well as correlation,triangulation and synchronization sensors for other nearby enabled ornon-enabled devices.

At some point, the network effects of enough enabled devices allow thenetwork to gather complete or nearly complete data (sufficient forprofiling and tracking) of a non-enabled device because of its regularintersection and sensing by enabled devices in its real-world location.

Above the applications layer 414, or hosted within it, is thecommunications delivery network 416. The communications delivery networkcan be operated by the W4 COMN operator or be independent third-partycarrier service. Data may be delivered via synchronous or asynchronouscommunication. In every case, the communication delivery network 414will be sending or receiving data on behalf of a specific application ornetwork infrastructure 408 request.

The communication delivery layer 418 also has elements that act assensors including W4 entity extraction from phone calls, emails, blogs,etc. as well as specific user commands within the delivery networkcontext. For example, “save and prioritize this call” said before end ofcall can trigger a recording of the previous conversation to be savedand for the W4 entities within the conversation to analyzed andincreased in weighting prioritization decisions in thepersonalization/user profiling layer 410.

FIG. 5 illustrates one embodiment of the analysis components of a W4engine as shown in FIG. 3. As discussed above, the W4 Engine isresponsible for identifying RWEs and relationships between RWEs and IOsfrom the data and communication streams passing through the W4 COMN.

In one embodiment the W4 engine connects, interoperates and instrumentsall network participants through a series of sub-engines that performdifferent operations in the entity extraction process. The attributionengine 504 tracks the real-world ownership, control, publishing or otherconditional rights of any RWE in any IO. Whenever a new IO is detectedby the W4 engine 502, e.g., through creation or transmission of a newmessage, a new transaction record, a new image file, etc., ownership isassigned to the IO. The attribution engine 504 creates this ownershipinformation and further allows this information to be determined foreach IO known to the W4 COMN.

The correlation engine 506 can operates two capacities: first, toidentify associated RWEs and IOs and their relationships (such as bycreating a combined graph of any combination of RWEs and IOs and theirattributes, relationships and reputations within contexts or situations)and second, as a sensor analytics pre-processor for attention eventsfrom any internal or external source.

In one embodiment, the identification of associated RWEs and IOsfunction of the correlation engine 506 is done by graphing the availabledata, using, for example, one or more histograms A histogram is amapping technique that counts the number of observations that fall intovarious disjoint categories (i.e. bins.). By selecting each IO, RWE, andother known parameters (e.g., times, dates, locations, etc.) asdifferent bins and mapping the available data, relationships betweenRWEs, IOs and the other parameters can be identified. A histogram of allRWEs and IOs is created, from which correlations based on the graph canbe made.

As a pre-processor, the correlation engine 506 monitors the informationprovided by RWEs in order to determine if any conditions are identifiedthat can trigger an action on the part of the W4 engine 502. Forexample, if a delivery condition has been associated with a message,when the correlation engine 506 determines that the condition is met, itcan transmit the appropriate trigger information to the W4 engine 502that triggers delivery of the message.

The attention engine 508 instruments all appropriate network nodes,clouds, users, applications or any combination thereof and includesclose interaction with both the correlation engine 506 and theattribution engine 504.

FIG. 6 illustrates one embodiment of a W4 engine showing differentcomponents within the sub-engines described above with reference to FIG.4. In one embodiment the W4 engine 602 includes an attention engine 608,attribution engine 604 and correlation engine 606 with severalsub-managers based upon basic function.

The attention engine 608 includes a message intake and generationmanager 610 as well as a message delivery manager 612 that work closelywith both a message matching manager 614 and a real-time communicationsmanager 616 to deliver and instrument all communications across the W4COMN.

The attribution engine 604 works within the user profile manager 618 andin conjunction with all other modules to identify, process/verify andrepresent ownership and rights information related to RWEs, IOs andcombinations thereof.

The correlation engine 606 dumps data from both of its channels (sensorsand processes) into the same data backbone 620 which is organized andcontrolled by the W4 analytics manager 622. The data backbone 620includes both aggregated and individualized archived versions of datafrom all network operations including user logs 624, attention rankplace logs 626, web indices and environmental logs 618, e-commerce andfinancial transaction information 630, search indexes and logs 632,sponsor content or conditionals, ad copy and any and all other data usedin any W4COMN process, IO or event. Because of the amount of data thatthe W4 COMN will potentially store, the data backbone 620 includesnumerous database servers and data stores in communication with the W4COMN to provide sufficient storage capacity.

The data collected by the W4 COMN includes spatial data, temporal data,RWE interaction data, IO content data (e.g., media data), and user dataincluding explicitly-provided and deduced social and relationship data.Spatial data can be any data identifying a location associated with anRWE. For example, the spatial data can include any passively collectedlocation data, such as cell tower data, global packet radio service(GPRS) data, global positioning service (GPS) data, WI-FI data, personalarea network data, IP address data and data from other network accesspoints, or actively collected location data, such as location dataentered by the user.

Temporal data is time based data (e.g., time stamps) that relate tospecific times and/or events associated with a user and/or theelectronic device. For example, the temporal data can be passivelycollected time data (e.g., time data from a clock resident on theelectronic device, or time data from a network clock), or the temporaldata can be actively collected time data, such as time data entered bythe user of the electronic device (e.g., a user maintained calendar).

Logical and IO data refers to the data contained by an IO as well asdata associated with the IO such as creation time, owner, associatedRWEs, when the IO was last accessed, etc. For example, an IO may relateto media data. Media data can include any data relating to presentablemedia, such as audio data, visual data, and audiovisual data. Audio datacan be data relating to downloaded music, such as genre, artist, albumand the like, and includes data regarding ringtones, ringbacks, mediapurchased, playlists, and media shared, to name a few. The visual datacan be data relating to images and/or text received by the electronicdevice (e.g., via the Internet or other network). The visual data can bedata relating to images and/or text sent from and/or captured at theelectronic device.

Audiovisual data can be data associated with any videos captured at,downloaded to, or otherwise associated with the electronic device. Themedia data includes media presented to the user via a network, such asuse of the Internet, and includes data relating to text entered and/orreceived by the user using the network (e.g., search terms), andinteraction with the network media, such as click data (e.g.,advertisement banner clicks, bookmarks, click patterns and the like).Thus, the media data can include data relating to the user's RSS feeds,subscriptions, group memberships, game services, alerts, and the like.

The media data can include non-network activity, such as image captureand/or video capture using an electronic device, such as a mobile phone.The image data can include metadata added by the user, or other dataassociated with the image, such as, with respect to photos, locationwhen the photos were taken, direction of the shot, content of the shot,and time of day, to name a few. Media data can be used, for example, todeduce activities information or preferences information, such ascultural and/or buying preferences information.

Relationship data can include data relating to the relationships of anRWE or IO to another RWE or IO. For example, the relationship data caninclude user identity data, such as gender, age, race, name, socialsecurity number, photographs and other information associated with theuser's identity. User identity information can also include e-mailaddresses, login names and passwords. Relationship data can furtherinclude data identifying explicitly associated RWEs. For example,relationship data for a cell phone can indicate the user that owns thecell phone and the company that provides the service to the phone. Asanother example, relationship data for a smart car can identify theowner, a credit card associated with the owner for payment of electronictolls, those users permitted to drive the car and the service stationfor the car.

Relationship data can also include social network data. Social networkdata includes data relating to any relationship that is explicitlydefined by a user or other RWE, such as data relating to a user'sfriends, family, co-workers, business relations, and the like. Socialnetwork data can include, for example, data corresponding with auser-maintained electronic address book. Relationship data can becorrelated with, for example, location data to deduce social networkinformation, such as primary relationships (e.g., user-spouse,user-children and user-parent relationships) or other relationships(e.g., user-friends, user-co-worker, user-business associaterelationships). Relationship data also can be utilized to deduce, forexample, activities information.

Interaction data can be any data associated with user interaction of theelectronic device, whether active or passive. Examples of interactiondata include interpersonal communication data, media data, relationshipdata, transactional data and device interaction data, all of which aredescribed in further detail below. Table 1, below, is a non-exhaustivelist including examples of electronic data.

TABLE 1 Examples of Electronic Data Spatial Data Temporal DataInteraction Data Cell tower Time stamps Interpersonal GPRS Local clockcommunications GPS Network clock Media WiFi User input of timeRelationships Personal area network Transactions Network access pointsDevice interactions User input of location Geo-coordinates

Interaction data includes communication data between any RWEs that istransferred via the W4 COMN. For example, the communication data can bedata associated with an incoming or outgoing short message service (SMS)message, email message, voice call (e.g., a cell phone call, a voiceover IP call), or other type of interpersonal communication related toan RWE. Communication data can be correlated with, for example, temporaldata to deduce information regarding frequency of communications,including concentrated communication patterns, which can indicate useractivity information.

The interaction data can also include transactional data. Thetransactional data can be any data associated with commercialtransactions undertaken by or at the mobile electronic device, such asvendor information, financial institution information (e.g., bankinformation), financial account information (e.g., credit cardinformation), merchandise information and costs/prices information, andpurchase frequency information, to name a few. The transactional datacan be utilized, for example, to deduce activities and preferencesinformation. The transactional information can also be used to deducetypes of devices and/or services the user owns and/or in which the usercan have an interest.

The interaction data can also include device or other RWE interactiondata. Such data includes both data generated by interactions between auser and a RWE on the W4 COMN and interactions between the RWE and theW4 COMN. RWE interaction data can be any data relating to an RWE'sinteraction with the electronic device not included in any of the abovecategories, such as habitual patterns associated with use of anelectronic device data of other modules/applications, such as dataregarding which applications are used on an electronic device and howoften and when those applications are used. As described in furtherdetail below, device interaction data can be correlated with other datato deduce information regarding user activities and patterns associatedtherewith. Table 2, below, is a non-exhaustive list including examplesof interaction data.

TABLE 2 Examples of Interaction Data Type of Data Example(s)Interpersonal Text-based communications, such as SMS and communicationdata e-mail Audio-based communications, such as voice calls, voicenotes, voice mail Media-based communications, such as multimediamessaging service (MMS) communications Unique identifiers associatedwith a communication, such as phone numbers, e-mail addresses, andnetwork addresses Media data Audio data, such as music data (artist,genre, track, album, etc.) Visual data, such as any text, images andvideo data, including Internet data, picture data, podcast data andplaylist data Network interaction data, such as click patterns andchannel viewing patterns Relationship User identifying information, suchas name, age, data gender, race, and social security number Socialnetwork data Transactional Vendors data Financial accounts, such ascredit cards and banks data Type of merchandise/services purchased Costof purchases Inventory of purchases Device Any data not captured abovedealing with user interaction data interaction of the device, such aspatterns of use of the device, applications utilized, and so forthDetermination and Display of Personalized Distance

In a mobile society, persons are continuously traveling from one pointto another. Often, a person wants or needs to know the distance betweentwo real-world locations. Numerous services exist to calculate spatialdistance. Such services may be, without limitation, web based services,such as Yahoo! Maps, Mapquest, or may be GPS-based services. Suchservices calculate spatial distance tied to a specific route and may beable to estimate travel time, either based on average travel time, orbased on real-time traffic data. Such services may additionally providefor a small degree of customization, such as finding routes that avoidhighways or tolls.

The distances calculated by such services are not, however, typicallypersonalized to any significant degree. A spatial distance does notfactor in a person's goals or objectives in traveling between twopoints. Furthermore, it does not factor in a person's schedule,interests, preferences or social networks in determining thedesirability of particular route. A personalized distance can bedetermined that takes such factors into account. The factors that can beused in determining a personal distance between two points can becategorized as spatial factors, temporal factors, social factors, andtopical (or logical) factors.

In one embodiment, calculation of a personalized distance between tworeal-world entities can begin with determining one or more routesbetween two real-world entities. One or more routes can be chosen basedon a user's preferred mode of travel. For example, a person may preferto walk or use public transportation rather than driving. Routing cansimply choose the shortest available route. Routing can additionallyreflect further travel preferences, such as avoiding highways, tolls,school zones, construction areas, and so forth. Given a known route,spatial distance can then be determined for the route. In oneembodiment, spatial distance is the length of the route. In anotherembodiment, the time to travel to a destination can be considered a formof spatial distance.

Spatial distance can be modified by spatial factors not directly relatedto distance. Such spatial factors may relate to additional spatialdimensions such as height, altitude, a floor on a building, and soforth. Such factors can relate to physical properties of the route orentities having a location on or near the route. For example, if aperson values scenery or visually stimulating surroundings, whethernatural, or manmade, a route that has a view of a bay or ocean orskyline can be more desirable. If a portion of a route has a reputationfor being in poor physical condition or is under construction, the routecan be considered less desirable. Spacial factors may additionallyinclude the additional dimension of velocity (i.e. direction and speed)of a user or other entities. Spatial factors may additionally includeenvironmental conditions tied to physical locations, such as localweather conditions.

Spatial distance can then be further modified using temporal factors,social factors, and topical factors. Temporal factors can be generallydefined as factors that relate to how the passage of time affects thedesirability of a route and the mode of transportation. The most basictemporal factor is the time it takes to travel a route. Travel time on aroute can be estimated based on average travel time historicallyassociated with a route. Alternatively, travel time can be moreprecisely determined by monitoring average speed and travel times fromreal-time monitors or sensors. Such sensors can be fixed sensorsspecifically installed along major avenues of travel for monitoringtraffic flow. Such sensors can also be user devices, such as cellulartelephones or GPSs whose location is continuously monitored and whichcan thus be used to determine the speed of travel for individual userdevices whose physical position is known. In one embodiment, data usedto determine travel time on a route may be a combination of many sourcesof data from multiple sensor networks.

Such travel time can be useful, but can be enhanced by combining it withhistorical travel time data accumulated over a period of time. Forexample, on Friday, people may historically leave the office earlier,and traffic predictably suffers a 15 to 20 minute slow down between 6:00PM and 7:00 PM on major routes out of a city. Thus, the speed of trafficat 5:45 PM may provide an overly optimistic estimate of travel timebetween 6:00 PM and 7:00 PM for a person whose commute would normally be30 minutes.

Travel time can also be affected by weather conditions. Thus, when itbegins to rain, historically, traffic may suffer a 30 minute slow onmajor routes out of a city. Thus, if rain is predicted or if it justbegins to rain, travel time for such routes may be adjusted accordingly.Travel time can also be affected by local events. For example, a concertmay be booked at a large arena downtown for a specific date starting at7:00 PM. Historical data may indicate that traffic slows down in thevicinity of the arena during concerts, increasing commute times by 10minutes.

Temporal factors can additionally include temporal data relating to thestarting point and ending points of a route. For example, if thedestination of a route is a restaurant or a retail location, if thelocation closes before the route can be fully traversed, the route isundesirable. If the wait time to be seated at a restaurant exceeds, forexample, 30 minutes, the route may also be undesirable. If an event isscheduled to occur at a location at a particular time, for example, livemusic begins at 10 PM, a route that arrives at the location after 10:00PM may be undesirable.

Temporal factors can additionally include temporal data relating to aspecific person. For example, if a person has an appointment, a routethat arrives early for the appointment is desirable. If a persontypically engages in specific activities at home, such as viewing aspecific television program, a route that takes a person to a locationaway from home, for example, a restaurant, that is so distant that theperson will not be able to reach home before the program airs may beundesirable.

Thus, the time it takes to traverse a route, informed by real-time andhistorical data, and the impact of such travel time on cotemporaneousevents can be determined for a specific route or a group of routes.Spatial distance, travel time, and events affected by travel time can,in one embodiment, be displayed individually. Alternatively, temporalfactors can be used to modify spatial distance to create a personalizeddistance. The personalized distance reflects the overall desirability ofthe route. In one embodiment, the distance increases as the desirabilityof the route decreases. For example, a route that reflects a spatialdistance of 10 miles may be increased to 30 miles because of slow traveltime or because the route will arrive late for an appointment based onreal-time travel estimates. A route which is expressed as a temporaldistance of 10 minutes may be increased to 30 minutes or “TL” for toolong if the route will arrive late for an appointment based on real-timetravel estimates.

In one embodiment, temporal factors can be used as weighting factors oradditive factors that are used to modify spatial distance in aconsistent manner. Weighting and additive factors can be used to reflecta simple, continuous numerical relationship. For example, if a 10 mileroute is projected to have a travel time of 30 minutes, reflecting anaverage speed of 20 mph, whereas 60 mph is taken to be an arbitrarytarget travel speed, a weighted distance of 30 miles could be computedby multiplying the travel time by the target travel speed. In anotherexample, an arbitrary increment of 1 mile can be added to spatialdistance for every additional minute a person is projected to be latefor an appointment. In another embodiment, a pre-defined code or tagcould be associated with the spatial distance, e.g. “10L” for tenminutes late, or “TL” for too late or too long.

Weighting and additive factors can additionally or alternatively, beused reflect a discrete intervals used multiplicatively or additionally.For example, if a person is projected to be late for an appointment from1 to 10 minutes, a multiplier of 1.5 or an addition of 10 miles could beapplied to spatial distance, whereas if a person is projected to be11-20 minutes late, a multiplier of 10 or an addition of 100 miles couldbe applied to spatial distance.

Spatial distance can thus be weighted by temporal factors in a largenumber of ways to produce a qualitative personal distance that reflectsspatial distance of a route and also reflects the impact of temporalfactors on the desirability (or even feasibility) of the route. In oneembodiment, the exact methodology for combining spatial distance andtemporal weighting factors can vary from person to person, and can becustomized to reflect the personality or habits of a person. Thus, aperson who hates driving may heavily weight travel time, whereas anobsessively punctual person may heavily weight being late for work orappointments. In one embodiment, the user can explicitly input suchpreferences. In another embodiment, such preferences may be imputed userbehavior which is reflected by sensor data and interaction data for theuser accumulated over time.

Spatial distance can additionally be modified using social factors.Social factors can be generally defined as factors that relate to how aperson's social relations affects the desirability of a route. A routecan be considered more desirable if the route is in proximity to one ormore individuals who are in a person's social network or otherwisedemonstrate a social relation with a user on the basis of spatial,temporal, or topical associations, correlations, overlaps or degrees ofseparation.

Such factors could be based on profile data associated with individualsin a person's social network. For example, a route that passes the homeaddress of a close friend can be considered more desirable, as it offersthe potential opportunity to drop in on a friend. Such factors couldalso be based on dynamic, real time data associated with persons in asocial network. For example, a route to a location may be consideredmore desirable if one or more friends or acquaintances are currentlypresent at that location.

Social factors may also make use of interaction or transaction dataassociated with individuals in a person's social network. For example, aroute to a location may be considered more desirable if the location isa business which is frequented or favorably reviewed by one or morefriends or relatives. In another example, a route containing roads thathave been unfavorably commented on by friends or are habitually avoidedby friends can be considered less desirable.

Social network factors can also be used in a negative fashion as well.Thus, if an individual is identified within a person's social network asa person to be avoided, routes that tend to avoid the individual andbusinesses and locales frequented by the individual may be consideredpreferable.

Spatial distance can additionally be modified using topical factors.Topical factors can be generally defined as including factors thatrelate to known information associated with locations, users, and otherentities in the environment. Such factors can relate to how a person'sinterests and preferences, as well as external events, affects thedesirability of a route. For example, topical factors may relate to thegeneral area surrounding the route. For if a person is safety conscious,a route that passes through an area that has a high crime rate can beconsidered less desirable. If a person enjoys shopping for hautecouture, a route that passes through an area that has a high density ofhigh end retailers or boutiques may be more desirable. Topical factorsmay relate to events occurring on or in the vicinity of the route. Forexample, if a festival is occurring in a neighborhood, a route thatpasses through the neighborhood may be more or less desirable, dependingon whether a person has an interest in the festival or not.

Topical factors may relate to the destination of the route. For example,a route to a location may be considered more desirable if the locationis a business which is associated with a topic of interest (or aversion)to the user. For example, if a person is a fan of blues music, a routeto a destination associated with blues music (i.e. a blue's club) can beconsidered more desirable. In another example, if a person doesn't likechildren, a route to a destination that is rated as a great familydestination can be considered less desirable. A route to a location maybe considered more desirable if the location is a business which isfavorably reviewed by a favorite reporter or news publication or afriend. For example, a route to a restaurant which has received glowingreviews in local publications can be considered more desirable, but maybe less desirable if a user's best friend gives the restaurant a badreview. Topical factors can thus be weighted by any known social factorrelated to the topic.

In one embodiment, social and topical factors can be used in addition totemporal factors as weighting factors or additive factors that are usedto modify spatial distance in a consistent manner to produce apersonalized distance. In one embodiment, the exact methodology forcombining spatial distance and temporal weighting factors can vary fromperson, and can be customized to reflect the personality, habits, andpreferences of a person.

Note that the methodologies described above can be extended to determinea personalized distance which is not tied to a physical route, or evento spatial or temporal dimensions. In one embodiment, the route is astraight line between the starting location and the ending location, arelative distance from a central third point, or a calculation based ona cluster of locations, and can be adjusted by social and topicalfactors.

In yet another embodiment, spatial and temporal dimensions are ignoredand the personalized distance between the starting location and theending location is based on social and topical factors relating to therequesting user, the starting and ending location, and all known RWEsand IOs associated with the user and the starting and ending locationSuch a personalized distance becomes, in effect, a metric which measureshow closely the starting and ending locations relate to the requestinguser's interests and associations.

The embodiments of the present invention discussed below illustrateapplication of the present invention within a W4 COMN. Nevertheless, itis understood that the invention can be implemented using any networkedsystem which is capable of tracking the physical location of users andmedia enabled electronic devices, and which is further capable ofcollecting and storing user profile data, location data, as well astemporal, spatial, topical and social data relating to users and theirdevices.

A W4 COMN can provide a platform that enables the determination ofpersonalized distances between two or more real world objects thatincludes spatial factors, temporal factors, social factors, and topicalfactors. The W4 COMN is able to achieve such a result, in part, becausethe W4 COMN is aware of the physical location of persons and therelative location of places, and is further aware of the preferences ofsuch persons and places and their relationship to one another and to thegreater network.

FIG. 7. illustrates one embodiment of the use of a W4 COMN for thedetermination of personalized distances between two or more real worldobjects.

In the illustrated embodiment, an individual 702 wishes to determine apersonalized distance between a starting location 720 and an endinglocation 724. In one embodiment, a user 704 enters a personalizedlocation request using a user proxy device 704, for example a PDA orportable media player, which is transmitted to a W4 COMN 750. In oneembodiment, the request comprises the starting location 720 and theending location 724. In alternative embodiments, the user may choose toenter more than two locations, which in one embodiment, can comprise astarting location 720 and an ending location 724 and one or moreadditional locations, for example an auditorium (i.e. to buy eventtickets) and a friend's house 718 (i.e. to stop and visit.)

At least one physical route 730 exists between the starting 720 andending locations 724. The route can be identified by a mappingapplication, such as, for example, Yahoo Maps, that is capable ofplotting routes along highways and roads between two locations.Alternatively, the route can be specified in the personalized locationrequest. The routes may be, without limitation, routes proceeding alongroads and highways, may be a pedestrian route, and may include segmentsutilizing mass transit. Where a route request comprises more than twolocations, each route will include all locations in the route request,and may provide alternate routes with differing ending locations. Forexample a route request starting at location 720 and including locations740, 718, and 724 could generate alternate routes ending at location 718and location 724.

There are fixed traffic sensors 730 along all or part of the route. Thesensors are in communication with the W4 COMN and continuously transmitreal-time data including at least traffic information to the W4 COMN.Additionally or alternatively, the W4 COMN can track the location ofnetwork user devices which are traveling on the route 730. For example,the network can determine the location of cell phones by triangulatingcellular signals or through the use of embedded GPS. Vehicles 708 mayadditionally contain sensors or geo-locatable devices which includes thevehicles rate, direction, and mode of motion. Such vehicles may includethe user's vehicle. Additionally or alternatively, the W4 COMN can trackalerts and traffic advisories transmitted by local authorities, or dataprovided by the local 911 network (not shown.) Additionally oralternatively, the W4 COMN can track the movement of air traffic 709 aswell as vehicular traffic.

The route begins at a starting location 720. The starting location canbe a physical point, an address, or a real-world entity, such as abuilding or an individual (e.g. the requesting user.) The route 730proceeds past a municipal auditorium 740 that periodically hosts eventssuch as concerts. The route additionally passes near the home 728 of afriend of the user 702. The route additionally passes a scenic area 744such as a shoreline, an overlook, or a clear view of a city skyline. Thelocation terminates at an ending location 724. The ending location canbe a physical point, an address, or a real-world entity, such as abuilding or an individual whose position is known to the network (e.g. afriend of the requesting user with a device whose position is knownthrough, e.g. GPS.)

The requesting user 702 has three friends 706, 710, and 726 known to thenetwork. User 706 is a friend of requesting user 702, but has noassociation with the route 730. User 726 has a home located 728 on theroute 730. User 710 is currently located at the ending location 724.User 710 has a proxy device 712, such as a smart phone, that is incommunication with the W4 COMN and whose geographical position can bedetermined, for example, by GPS technologies or triangulation ofcellular signals.

Physical locations of any type, such as starting location 720 and anending location 724, can further contain or be associated with proxydevices known to the network. Such devices can include proxy devicesassociated with, without limitation, other users' proxy devices, vendingmachines, printers, appliances, LANs, WANs, WiFi devices, and RFID tagswhich can provide additional information to the network. All of theentities shown in FIG. 7 may be known to the W4 COMN, and all networkconnectable devices and sensors may be connected to, or tracked by, theW4 COMN (note, all possible connections are not shown in FIG. 7.)

FIG. 8 illustrates one embodiment of how the objects shown in FIG. 7 canbe defined to a W4 COMN.

Individuals 702, 706, 712 and 726 are represented as user RWEs 802, 806,810 and 826 respectively. Each individual's devices are represented asproxy RWEs 804, and 812. Locations 720, 724, and 740 are represented aslocation (or business) RWEs 820, 824, and 840. The traffic sensor 730 isrepresented as a sensor RWE 830. The route 730 is represented as a IO830 containing route information. The scenic area is represented by anRWE which includes information on the location and other attributes ofthe scenic area. All RWEs can have additionally have, withoutlimitation, IOs associated with RWEs proxies, friends, and friendsproxies.

FIG. 9 illustrates one embodiment of a data model showing how the RWEsshown in FIG. 8 can be related to entities and objects within a W4 COMN.

The RWE for the requesting user is associated with a route IO 830. Theroute IO 830 includes, in one embodiment, sufficient data to fullydefine the physical route, such as road segments and distances or a setof GPS coordinates. The route IO is directly associated with a set ofRWEs: RWE 820 representing the starting location of the route; RWE 830representing a traffic sensor on the route; RWE 840 representing amunicipal auditorium on or near the route and a scenic area 844; and RWE824 representing the ending location.

In the illustrated embodiment, the route IO is further associated withtwo IOs relating to topics: IO 828 representing the user profile of anRWE 820 representing a friend 820 of the requesting user whose homeaddress is located on or near the route. Note that the route IO may bedirectly related to any or all IOs associated with physical locationsalong the route, but is also indirectly related to an unbounded set ofIOs related to spatial, temporal, and topical factors related to theroute and requesting user. For example, in FIG. 9, the route isindirectly related to user 802's friends 806, 810, and 820 through user802's social network. In FIG. 9, every IO shown is related directly orindirectly to the route 830.

The requesting user RWE is associated with friend/user RWEs 806, 810,and 820 through a social network represented by an IO relating to atopic 803. User RWE 806 is associated with one or more interaction dataIO that can include, without limitation, communications relating toending location RWE 824 and oter users or locations. User RWE 810 isassociated with the ending location RWE 824, for example, by anassociation indicating the user is physically present at the location.User RWE 810 is also associated with a user proxy device RWE 812 whosephysical location is known.

The location RWE 840 for the municipal auditorium is further associatedwith an IO having information on events occurring at the auditorium,including a calendar with dates and times of events. The location RWE824 for the destination is further associated with one or more IOsrelating to topics 828 which may include, without limitation, a calendarof live music to be performed at the destination, ratings by customersof the destination location, or reviews of the location by local media.

In one embodiment, the relationships shown in FIG. 9 are created by theW4 COMN using a data modeling strategy for creating profiles and othertypes of IOs relating to topics for users, locations, any device on thenetwork and any kind of user-defined data. Using social, spatial,temporal and topical data available about a specific user, topic orlogical data object, every entity known to the W4 COMN can be mapped andrepresented against all other known entities and data objects in orderto create both a micro graph for every entity, as well as a global graphthat relates all known entities with one another and relatively fromeach other. In one embodiment, such relationships between entities anddata objects are stored in a global index within the W4 COMN.

FIG. 10 illustrates one embodiment of a process 900 of how a networkhaving temporal, spatial, and social data, for example, a W4 COMN, canbe used for the determination of personalized distances between two ormore real world objects.

A request is received for the calculation of a personalized distance 910between real-world entities, wherein the request comprises tworeal-world entities corresponding to a starting location and an endinglocation. The request may additionally include a physical route betweenthe starting location and an ending location or other criteria. Therequest may be for the current time, or may be for a future point intime. One or more physical routes between the starting location and theending location are mapped 920. For every route 930, data is retrieved940 from network databases 942 and network sensors 944 for entities andobjects associated with the route, wherein the network databases containspatial, temporal, social, and topical data relating to entities andobjects within the network. In one embodiment, the network databases 942include a global index of RWE and IO relationships maintained by the W4COMN. The spatial, temporal, social, and topical data is used tocalculate a personalized distance 950 using one or more embodiments ofmethodologies discussed above. The personalized distance is thendisplayed 960 for each route.

FIG. 11 illustrates one embodiment of a personal distance determinationengine 1000 that is capable of supporting the process in FIG. 10. In oneembodiment, the personal distance determination engine 1000 is acomponent of a W4 engine 502 within a W4 COMN and may use modules withinthe W4 engine to support its functions.

A request receiving module 1100 receives requests for the calculation ofpersonalized distances between real-world entities, wherein the requestcomprises at least two real-world entities corresponding to a startinglocation and an ending location. The request may additionally include aphysical route between the starting location and the ending location. Aroute determination module 1200 maps one or more physical routes betweenthe starting location and ending location. A route data retrieval module1300 retrieves spatial, temporal, social, and topical data from networkdatabases 1320 and sensors 1340 for entities and objects associated witha route. A personalized distance calculation module 1400 uses retrievedspatial, temporal, social, and topical data to calculate a personalizeddistance using one or more embodiments of methodologies discussed above.A personalized distance display module 1500 displays personalizeddistance on a display medium 1520.

In one embodiment, the request receiving module provides an interfacefor entry of personalized distance requests. The interface may be agraphical user interface displayable on computers or PDAs, includingHTTP documents accessible over the Internet. Such an interfaces may alsotake other forms, including text files, such as emails, and APIs usableby software applications located on computing devices. The interfaceprovide. In one embodiment, a personalized distance request can beentered on a mapping application interface, such as Yahoo Maps. Therequest may be for the current time, or may be for a future point intime.

In one embodiment, route determination module may determine routes usinga mapping engine such as that provided by Yahoo! Maps that is capable ofmapping a route between two locations. Alternatively, the route may bepresumed to be a straight line between two locations, a relativedistance from a central third point, or a calculation based on a clusterof locations. Alternatively, no physical route may be determined. In oneembodiment, the route determination module returns multiple physicalroutes. The routes can be routes consisting entirely of roads andhighways, pedestrian ways, public transportation, or any combinationthereof.

In one embodiment, the distance display module displays personalizeddistance on a user interface. The interface can be a graphical userinterface displayable on computers or PDAs, including HTTP documentsaccessible over the Internet. Such an interface may also take otherforms, including text files, such as emails, and APIs usable by softwareapplications located on computing devices. In one embodiment, thepersonalized distance for one or more routes can be listed as text ornumbers. The factors used to calculate the personalized distance can belisted on same display as text or numbers so that the user canunderstand the basis of the calculation. In one embodiment, distancesabove and below a user defined threshold can be automatically excludedor preferred.

In one embodiment, a personalized distance can be displayed as anoverlay of a graphical display of a map of the route to which thepersonalized distance relates. For example, the personalized distancecould be displayed as a colored highlight over the length of the routewherein the color indicates the magnitude of the distance. For example,red could signify a distance of 20 miles or greater, or, alternatively,a route wherein the personalized distance is greater than twice thespatial distance. The personalized distance could also be displayed as atext tag on the mute. Entities and objects which were used in thepersonalized distance calculation and which have a physical locationclose to the route can additionally be displayed as text tags or symbolson the map. In an alternative embodiment, the color coding of routesbased on rank of users' likely preferences (e.g. the best route iscolored green, the worst, brown.)

In one embodiment, in a W4 COMN, the route data retrieval module 1300can be component of a correlation engine 506, and makes use of datarelationships within the W4 COMN to retrieve data related to a route. Inone embodiment, the network databases 1320 include a global index of RWEand IO relationships maintained by the W4 COMN.

For example, referring back to FIG. 9, a route IO 830 can be associatedwith a number of objects and entities that relate to data that can beused in calculating a personalized distance for the route. In theillustrated embodiment, the route IO relates to real-time sensors 832that are periodically or continuously polled for data. Sensor data caninclude traffic data, user presence and motion data, as well asenvironmental data, such as temperature, visibility, and weather.Traffic sensor data can be used to calculate transit time on the route.Other types of sensed data can additionally be used as factors incomputing personalized distance. For example, if it starts to rain,transit times can be increased based on historical data. Additionally oralternatively, if the requesting user RWE 820 hates driving in rain(e.g. as indicated in profile or interaction data), rain can be can be asubjective factor in a personalized distance calculation.

The route IO 830 further relates to a location RWE 840, an auditoriumhaving a location near the route. The RWE 842 is associated with anevents IO that can include a calendar of events. If there is an eventscheduled for the time the route will traversed, the event can be afactor in a personalized distance calculation. The route IO 830 furtherrelates to an IO relating to a topic for a scenic location near theroute. If the requesting user 802 values scenic views (e.g. as indicatedin profile or interaction data), the scenic location can be a factor ina personalized distance calculation.

In the illustrated embodiment, the route IO 830 is owned by therequesting user RWE 802. The user RWE 802 is associated through a socialnetwork with three user RWEs 806, 810 and 820 that are friends of therequesting user. The friend RWEs each relate to data that can be factorsin calculating a personalized distance for the route. User RWE 806 canhave interaction data or profile data relating to the destination RWE824, such as emails or text messages expressing opinions about thedestination (e.g. bad food, great music.) User RWE 810 is physicallypresent at the destination, possibly increasing the attractiveness ofthe location. The profile IO 828 of user/friend RWE 820 indicates theuser RWE's home is near physically near the route, and hence, it wouldbe easy for the requesting user to drop in.

The destination location RWE 824 has topical and other IOs 828associated with it that contain additional data that can be factors in apersonalized distance calculation. A music calendar may indicate amusical performance at a specific time. Users outside of the requestingRWE's social networks may have rated the destination location for food,ambience, and service. Local media may have reviewed the destinationlocation.

In one embodiment, the personalized distance calculation module 1400 canweight spatial, temporal, social, and topical factors differently. Suchweighting may be determined automatically based on the context of therequest. Since each context will have a potentially unbounded set ofassociated data, the personalized distance calculation module 1400 can,if sufficient information is present, determine the category of the mostimportant factor depending on context. For example, shop hours (atemporal factor) become the primary factor for a destination of adistance to a location that is about to close, but are mostly ignoredfor calculations in the middle of business hours. When, for example, afriend is presently shopping there (a social factor), such a socialfactor becomes the most important factor for weighting a spatialdistance.

In one embodiment every RWE and IO associated with a personalizeddistance calculation has at least one data point for spatial, temporal,social, and topical factors, and can have large sets of data points foreach type of factor. Such factors can be sorted and ranked for weightinga personalized distance calculation. Alternatively, or additionally, ausers weighting preferences are stored on the network in a weightingprofile, which can be additionally maintained using a user interfacesuch as that shown in FIG. 12. The interface 2000 can be used to applydifferential weights to spatial 2400, temporal 2300, social 2100, andtopical factors 2200 using sliders 2420, 2320, 2120, and 2200.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible. Functionality may also be, inwhole or in part, distributed among multiple components, in manners nowknown or to become known. Thus, myriad software/hardware/firmwarecombinations are possible in achieving the functions, features,interfaces and preferences described herein. Moreover, the scope of thepresent disclosure covers conventionally known manners for carrying outthe described features and functions and interfaces, as well as thosevariations and modifications that may be made to the hardware orsoftware or firmware components described herein as would be understoodby those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

We claim:
 1. A method comprising: receiving, by a computing device overa network, a request from a user for a route from a starting locationand an ending location, the request comprising information associatedwith geographical position information of the starting location and theending location; determining, via the computing device, a first routebeginning at the starting location and ending at the ending location,said first route comprising information related to a path between andincluding the starting location and the ending location; determining,via the computing device, message data that is associated with the firstroute, said determination comprising analyzing message activity by usersand identifying, based on said analyzing, the message data that isrelated to the first route, the analysis further comprising determiningspatial, temporal, topical and social data from the message data;determining, via the computing device, a second route based on at leastone of the spatial, temporal, topical and social data from the messagedata, said second route beginning along the path and ending at theending location; and communicating, via the computing device, the secondroute to the requesting user for display on a device of the user.
 2. Themethod of claim 1, wherein said user request further comprises apreferred mode of travel.
 3. The method of claim 2, further comprising:determining spatial, temporal, topical and social data from the messagedata that is associated with the preferred mode of travel; determining athird route based on at least one of the spatial, temporal, topical andsocial data associated with the preferred mode of travel, said thirdroute beginning along the path and ending at the ending location; andcommunicating the third route to the requesting user for display on thedevice of the user.
 4. The method of claim 1, further comprising:determining a geographic position along the path of the first routebetween the starting location and the ending location based on at leastone of the spatial, temporal, topical and social data; determining afourth route based on at least one of the spatial, temporal, topical andsocial data associated with the geographic position, said fourth routebeginning along the path, avoiding the geographic position and ending atthe ending location; and communicating the fourth route to therequesting user for display on the device of the user.
 5. The method ofclaim 4, wherein said geographic position is a specific geographiclocation along the first route that is identifiable from the spatial,temporal, topical and social data of the message data.
 6. The method ofclaim 1, wherein said second route is temporally shorter than the firstroute.
 7. The method of claim 1, wherein said spatial, temporal, topicaland social data corresponds to traffic associated with the first route.8. The method of claim 1, wherein said starting location and the endinglocation are each associated with a real world entity.
 9. The method ofclaim 1, wherein said path is a physical route between a startinglocation and an ending location.
 10. The method of claim 1, wherein saidmessage activity is selected from communications from users consistingof: email, Instant Messages (IMs), text messages, telephone calls, videocalls, social networking messages and gaming.
 11. A non-transitorycomputer-readable storage medium tangibly encoded withcomputer-executable instructions, that when executed by a processorassociated with a computing device, performs a method comprising:receiving a request from a user for a route from a starting location andan ending location, the request comprising information associated withgeographical position information of the starting location and theending location; determining a first route beginning at the startinglocation and ending at the ending location, said first route comprisinginformation related to a path between and including the startinglocation and the ending location; determining message data that isassociated with the first route, said determination comprising analyzingmessage activity by users and identifying, based on said analyzing, themessage data that is related to the first route, the analysis furthercomprising determining spatial, temporal, topical and social data fromthe message data; determining a second route based on at least one ofthe spatial, temporal, topical and social data from the message data,said second route beginning along the path and ending at the endinglocation; and communicating the second route to the requesting user fordisplay on a device of the user.
 12. The non-transitorycomputer-readable storage medium of claim 11, wherein said user requestfurther comprises a preferred mode of travel.
 13. The non-transitorycomputer-readable storage medium of claim 12, further comprising:determining spatial, temporal, topical and social data from the messagedata that is associated with the preferred mode of travel; determining athird route based on at least one of the spatial, temporal, topical andsocial data associated with the preferred mode of travel, said thirdroute beginning along the path and ending at the ending location; andcommunicating the third route to the requesting user for display on thedevice of the user.
 14. The non-transitory computer-readable storagemedium of claim 11, further comprising: determining a geographicposition along the path of the first route between the starting locationand the ending location based on at least one of the spatial, temporal,topical and social data; determining a fourth route based on at leastone of the spatial, temporal, topical and social data associated withthe geographic position, said fourth route beginning along the path,avoiding the geographic position and ending at the ending location; andcommunicating the fourth route to the requesting user for display on thedevice of the user.
 15. The non-transitory computer-readable storagemedium of claim 14, wherein said geographic position is a specificgeographic location along the first route that is identifiable from thespatial, temporal, topical and social data of the message data.
 16. Thenon-transitory computer-readable storage medium of claim 11, whereinsaid second route is temporally shorter than the first route.
 17. Thenon-transitory computer-readable storage medium of claim 11, whereinsaid spatial, temporal, topical and social data corresponds to trafficassociated with the first route.
 18. A system comprising: a processor; anon-transitory computer-readable storage medium for tangibly storingthereon program logic for execution by the processor, the program logiccomprising: receiving logic executed by the processor for receiving arequest from a user for a route from a starting location and an endinglocation, the request comprising information associated withgeographical position information of the starting location and theending location; determining logic executed by the processor fordetermining a first route beginning at the starting location and endingat the ending location, said first route comprising information relatedto a path between and including the starting location and the endinglocation; determining logic executed by the processor for determiningmessage data that is associated with the first route, said determinationcomprising analyzing message activity by users and identifying, based onsaid analyzing, the message data that is related to the first route, theanalysis further comprising determining spatial, temporal, topical andsocial data from the message data; determining logic executed by theprocessor for determining a second route based on at least one of thespatial, temporal, topical and social data from the message data, saidsecond route beginning along the path and ending at the ending location;and communication logic executed by the processor for communicating thesecond route to the requesting user for display on a device of the user.19. The system of claim 18, further comprising: determining logicexecuted by the processor for determining spatial, temporal, topical andsocial data from the message data that is associated with a preferredmode of travel; determining logic executed by the processor fordetermining a third route based on at least one of the spatial,temporal, topical and social data associated with the preferred mode oftravel, said third route beginning along the path and ending at theending location; and communication logic executed by the processor forcommunicating the third route to the requesting user for display on thedevice of the user.
 20. The system of claim 18, further comprising:determining logic executed by the processor for determining a geographicposition along the path of the first route between the starting locationand the ending location based on at least one of the spatial, temporal,topical and social data; determining logic executed by the processor fordetermining a fourth route based on at least one of the spatial,temporal, topical and social data associated with the geographicposition, said fourth route beginning along the path, avoiding thegeographic position and ending at the ending location; and communicationlogic executed by the processor for communicating the fourth route tothe requesting user for display on the device of the user.